Abstract：
BACKGROUND: Near-infrared spectroscopy (NIRS) is considered to be a fast and reliable non-destructive technique for fruit analysis. Considering that consumers are looking for strawberries with good sweetness, texture, and appearance, producers need to effectively measure the ripeness stage of strawberries to guarantee their final quality. Therefore, the use of this technique can contribute to decreasing the high level of waste and delivering good ripe strawberries to consumers. The present study aimed to evaluate the predictive capacity of NIRS technology, as a possible alternative to conventional methodology, for the analysis of the main organoleptic parameters of strawberries (Fragaria × ananassa Duch.).
RESULTS: Spectroscopic measurements and physicochemical analyses [total soluble solids (TSS), titratable acidity, colour, texture] of \'Victory\' strawberries were carried out. The predictive models developed for titratable acidity, colour and texture were not good enough to quantify those parameters. By contrast, in the NIRS quantitative prediction analysis of TSS, it was observed that the spectral pre-treatment with the highest predictive capacity was the first derivative 1-5-5. The coefficients of determination were: 0.9277 for the calibration model; 0.5755 for the validation model; and 0.8207 for the prediction model, using a seven-factor partial least squares multivariate regression analysis.
CONCLUSIONS: Therefore, these results demonstrate that NIR analysis could be used to predict the TSS in strawberry, and further work on sampling is desirable to improve the prediction obtained in the present study. It is shown that NIRS technology is a suitable tool for determining quality attributes of strawberry in a fast, economic, and environmentally friendly way. © 2022 Society of Chemical Industry.
RESULTS: Spectroscopic measurements and physicochemical analyses [total soluble solids (TSS), titratable acidity, colour, texture] of \'Victory\' strawberries were carried out. The predictive models developed for titratable acidity, colour and texture were not good enough to quantify those parameters. By contrast, in the NIRS quantitative prediction analysis of TSS, it was observed that the spectral pre-treatment with the highest predictive capacity was the first derivative 1-5-5. The coefficients of determination were: 0.9277 for the calibration model; 0.5755 for the validation model; and 0.8207 for the prediction model, using a seven-factor partial least squares multivariate regression analysis.
CONCLUSIONS: Therefore, these results demonstrate that NIR analysis could be used to predict the TSS in strawberry, and further work on sampling is desirable to improve the prediction obtained in the present study. It is shown that NIRS technology is a suitable tool for determining quality attributes of strawberry in a fast, economic, and environmentally friendly way. © 2022 Society of Chemical Industry.
摘要：
背景：近红外光谱（NIRS）被认为是一种用于水果分析的快速可靠的无损技术。考虑到消费者正在寻找甜味好的草莓，纹理,和外观，生产者需要有效地测量草莓的成熟阶段，以保证其最终质量。因此,使用这种技术可以有助于减少高水平的废物，并向消费者提供良好的成熟草莓。本研究旨在评估NIRS技术的预测能力，作为传统方法的可能替代，用于分析草莓的主要感官参数（Fragaria×ananassaDuch。).
结果：光谱测量和理化分析[总可溶性固体（TSS），可滴定酸度，颜色,进行了\'胜利\'草莓的质地]。为可滴定酸度开发的预测模型，颜色和质地不足以量化这些参数。相比之下,在NIRS对TSS的定量预测分析中，观察到具有最高预测能力的光谱预处理是一阶导数1-5-5。确定系数为：校准模型为0.9277；验证模型为0.5755；预测模型为0.8207，使用七因素偏最小二乘多元回归分析。
结论：因此，这些结果表明，NIR分析可用于预测草莓中的TSS，并且需要进一步的采样工作来改善本研究中获得的预测。结果表明，NIRS技术是快速确定草莓品质属性的合适工具，经济,和环保的方式。©2022化学工业学会。
结果：光谱测量和理化分析[总可溶性固体（TSS），可滴定酸度，颜色,进行了\'胜利\'草莓的质地]。为可滴定酸度开发的预测模型，颜色和质地不足以量化这些参数。相比之下,在NIRS对TSS的定量预测分析中，观察到具有最高预测能力的光谱预处理是一阶导数1-5-5。确定系数为：校准模型为0.9277；验证模型为0.5755；预测模型为0.8207，使用七因素偏最小二乘多元回归分析。
结论：因此，这些结果表明，NIR分析可用于预测草莓中的TSS，并且需要进一步的采样工作来改善本研究中获得的预测。结果表明，NIRS技术是快速确定草莓品质属性的合适工具，经济,和环保的方式。©2022化学工业学会。
